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Optimizing the spatial structure of urban agglomeration: based on social network analysis

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Abstract

The development of urban agglomerations is fundamentally influenced by their spatial structure. Based on the case of Beijing–Tianjin–Hebei (BTH) urban agglomeration, this study aims to evaluate the spatial connection intensity and spatial structure characteristics of BTH urban agglomeration and provide tenable suggestions to optimize the BTH urban agglomeration. The TOPSIS model, the revised gravity model, and social network analysis are employed respectively. The findings indicate that: (1) Wide discrepancy exists within the agglomeration in terms of spatial connection intensity. Cities in the central regions have the greatest connection intensity with other cities and then the connection intensity decreases from the middle to the northern and southern parts of the agglomeration. The connection intensity between core cities such as Beijing, Tianjin and Shijiazhuang and the other non-core cities is much lower than the connection intensity among core cities. (2) The social network of BTH urban agglomeration is relatively low-dense and unstable. The centrality degrees of Beijing and Tianjin are the highest, making them the core of the spatial structure of the agglomeration. (3) The BTH urban agglomeration can be divided as three circle layers and four cohesive subgroups, among which Shijiazhuang and Hengshui are two cities dissociating from other cities. Given this, it is urgent that the government should disengage Beijing from its non-capital functions, promote Tangshan and Baoding as bridge cities, so as to enhance the connection intensity between core cities and edge cities, turn Shijiazhuang into the third growth pole other than Beijing and Tianjin, and eventually revolutionize the monopolar structure into a multipolar one.

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Authors and Affiliations

  1. School of Public Administration, Hohai University, Nanjing, 211100, China

    Xinhua Zhu, Qianli Wang & Peifeng Zhang

  2. School of International Economics and Trade, Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, China

    Yunjiang Yu

  3. Guangxi University of Finance and Economics, Nanning, 530003, China

    Lingling Xie

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  1. Xinhua Zhu
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  2. Qianli Wang
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  3. Peifeng Zhang
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  4. Yunjiang Yu
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  5. Lingling Xie
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Correspondence to Yunjiang Yu.

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Zhu, X., Wang, Q., Zhang, P. et al. Optimizing the spatial structure of urban agglomeration: based on social network analysis. Qual Quant 55, 683–705 (2021). https://doi.org/10.1007/s11135-020-01016-3

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